Quick answer: To build an MCP server, choose one narrow integration domain, define a small set of tools and resources, implement them with an official SDK, test locally over stdio, and add Streamable HTTP plus OAuth only when remote access is required. Treat descriptions, schemas, authorization and error responses as product interfaces: they determine whether an AI agent can use the server reliably and safely.
An MCP server is not merely an API wrapper with new names. It is an interface designed for AI hosts that discover capabilities at runtime. A technically valid server can still fail if its tools are ambiguous, return too much context or grant broader authority than the task requires.
If the protocol is new to you, start with what is Model Context Protocol and how MCP works.
1. Start with one bounded workflow
Write down the user outcome before writing code. Good first server scopes include:
- searching a product knowledge base;
- reading issues from one repository provider;
- checking deployment status;
- creating drafts in one content system;
- retrieving an SEO audit by project ID.
Avoid an initial server described as “access everything in our platform.” Broad surfaces create naming collisions, authorization mistakes and excessive tool selection. You can expand after observing real calls.
For each workflow, document:
- who the user is;
- what object they want to read or change;
- the minimum required permission;
- whether the operation has side effects;
- the expected result and failure states;
- the authoritative system behind the server.
2. Decide between tools, resources and prompts
MCP servers can expose three core server-side capabilities.
| Capability | Use it for | Example |
|---|---|---|
| Tool | Dynamic queries or actions | search_projects, create_draft |
| Resource | Readable content with a stable URI | A project brief or schema |
| Prompt | Reusable user-invoked workflow template | “Prepare a launch review” |
Do not turn every endpoint into a tool. A stable document may be better represented as a resource. A reusable workflow may be clearer as a prompt. Keep consequential write operations separate from read operations so clients can apply different approval policies.
3. Design agent-friendly tools
Tool design matters more than tool count. Each tool should have:
- a clear verb-object name;
- a description stating when to use it and when not to;
- a constrained JSON input schema;
- explicit required fields;
- structured output with stable identifiers;
- useful, recoverable error messages;
- a truthful read-only or side-effect contract.
Prefer get_project(project_id) and search_projects(query, limit) over one overloaded projects(action, data) tool. The first design gives the model fewer decisions inside each call and makes authorization easier to review.
Use enums for finite choices, bounds for numeric inputs and ISO formats for dates. Do not accept a free-form shell command or SQL statement merely because it is convenient. For deeper comparisons with application-native tools, read MCP vs function calling.
4. Choose an SDK and transport
The MCP project publishes official SDKs for major languages. Choose the language that fits the underlying system and your operations team, not the language used in a tutorial.
Start with stdio for a local server. The host launches the server as a subprocess and exchanges protocol messages through standard input and output. Write operational logs to standard error so they do not corrupt the protocol stream.
Use Streamable HTTP when multiple users or remote hosts need a shared service. A production remote server also needs HTTPS, authentication, authorization, rate limits, tenant isolation and service observability.
| Requirement | Likely starting point |
|---|---|
| Local files or desktop workflow | stdio |
| Shared SaaS integration | Streamable HTTP |
| Private company knowledge | Remote HTTP with user authorization |
| One application-only helper | Native function may be simpler |
Read MCP vs API before committing to a server that only one tightly coupled application will use.
5. Implement the smallest useful server
A conceptual implementation looks like this:
create server with name and version
register search_projects tool with input schema
authenticate request
authorize user for tenant
validate query and limit
call underlying service
return stable IDs, titles and canonical URLs
start stdio or Streamable HTTP transport
The exact SDK API changes by language and version, so copy current syntax from the official SDK documentation. Pin versions, record the supported protocol version and run dependency scanning.
For a knowledge server, a compact search and fetch pattern is often effective. Search returns ranked IDs, titles, snippets and canonical URLs; fetch resolves a chosen ID into content and metadata. OpenAI documents this pattern for research and company-knowledge integrations.
6. Add authentication and authorization correctly
Authentication proves who is calling. Authorization decides what that user may do. Enforce both on the server for every request.
For local stdio servers, protect secrets through the host's credential mechanism or environment configuration and restrict the process. For remote HTTP servers, follow the current MCP authorization specification and use an OAuth design appropriate to your deployment.
Never pass a token received for your MCP server directly to an upstream service as if it were an upstream token. The MCP security guidance explicitly identifies token passthrough as an anti-pattern. Validate token audience, scope, expiry and tenant, then use a legitimate delegated flow or server-side credential designed for the upstream API.
Use least privilege:
- separate read and write scopes;
- authorize object ownership, not only tool access;
- default new tools to unavailable;
- require fresh approval for destructive actions;
- avoid storing credentials in logs or tool output.
7. Make errors useful to agents
An agent needs to know whether to retry, correct an argument, ask the user or stop. Return structured errors that distinguish:
- invalid input;
- authentication required;
- permission denied;
- object not found;
- rate limited;
- temporary upstream failure;
- conflicting state;
- policy-blocked operation.
Do not expose stack traces, tokens or internal database details. Include a safe message and, where appropriate, a retry hint or the field that must be corrected.
8. Test at three levels
Protocol tests
Verify initialization, capability negotiation, list operations, invocation, invalid requests and shutdown behavior.
Tool contract tests
Test schemas, authorization boundaries, pagination, empty results, malicious inputs and upstream failures. Confirm that declared read-only tools cannot write.
Agent workflow tests
Give a representative model realistic tasks. Measure whether it selects the correct tool, supplies valid arguments, recovers from errors and produces a verifiable result. The AI agent evaluation guide explains task suites, trajectory checks and regression gates.
Include security cases such as indirect prompt injection in retrieved content, cross-tenant identifiers, oversized responses and unapproved write attempts.
9. Add production controls
A production MCP server should capture:
- server and tool version;
- authenticated principal and tenant;
- tool name, latency and outcome;
- approval or policy decision;
- upstream request identifier;
- rate-limit and error category;
- redacted audit evidence for consequential actions.
Do not log complete prompts or returned records by default. Apply retention, regional processing and deletion policies to MCP traffic just as you would to the underlying service.
Use timeouts, bounded pagination, idempotency keys for safe retries, concurrency limits and circuit breakers. Monitor not only uptime but also tool failure rate and semantic regressions after description or schema changes.
10. Document and distribute the server
Publish installation details, supported clients, transport, required scopes, tool inventory, data handling, version policy and security contact. If you publish through an MCP registry, remember that discovery does not eliminate due diligence. Users still need to verify the publisher and package source.
Products listed on IndieTools can also illustrate integration opportunities. dullnote describes AI-readable project context, while SEOReport describes an MCP endpoint for audit data. A server for either pattern should keep read access narrow and return canonical source metadata.
MCP server launch checklist
- One bounded domain and named owner
- Small, non-overlapping tool set
- Validated input and output schemas
- Separate read and write authority
- stdio or Streamable HTTP selected intentionally
- Authentication and object-level authorization
- Approval path for consequential actions
- Protocol, contract, workflow and security tests
- Versioning, audit logs and rollback plan
- Public setup and data-handling documentation
Before launch, use the MCP security best practices as a dedicated review.
Frequently asked questions
Do I need a new backend to build an MCP server?
No. An MCP server commonly calls an existing API, database or service layer. Keep business rules and authorization in authoritative backend systems where possible.
Should I build locally or remotely first?
Start with stdio when the workflow and data are local. Start with remote HTTP when the product is inherently a shared service, but include authentication and tenancy from the first version.
How many tools should an MCP server expose?
There is no universal number. Begin with the smallest coherent set. Tool search and filtering can help large catalogs, but they do not repair ambiguous design.
Can I publish write tools?
Yes, but isolate them, enforce narrow scopes, validate current state and require approval for consequential operations.
dullnote
SEOReport
